



'''simple docstring'''






import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


lowerCAmelCase:   Any					=		'base_with_context'
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Dict	  =	nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']							)							)
  a		:   Tuple	  =	nn.Parameter(
      torch.FloatTensor(weights['Embed_0']['embedding']							)					,     requires_grad=_A							)
  for lyr_num, lyr in enumerate(model.encoders							):
    a		:   int	  =	weights[f"""layers_{lyr_num}"""]
    a		:   List[str]	  =	nn.Parameter(
        torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']							)							)

    a		:   List[str]	  =	ly_weight['attention']
    a		:   Any	  =	nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T							)							)
    a		:   List[str]	  =	nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T							)							)
    a		:   str	  =	nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T							)							)
    a		:   List[Any]	  =	nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T							)							)

    a		:   Union[str, Any]	  =	nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']							)							)

    a		:   List[Any]	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T							)							)
    a		:   str	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T							)							)
    a		:   Tuple	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T							)							)

  a		:   List[str]	  =	nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']							)							)
  return model
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Tuple	  =	nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T							)							)

  a		:   Optional[Any]	  =	nn.Parameter(
      torch.FloatTensor(weights['Embed_0']['embedding']							)					,     requires_grad=_A							)

  for lyr_num, lyr in enumerate(model.encoders							):
    a		:   Union[str, Any]	  =	weights[f"""layers_{lyr_num}"""]
    a		:   Union[str, Any]	  =	ly_weight['attention']

    a		:   str	  =	nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T							)							)
    a		:   Optional[Any]	  =	nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T							)							)
    a		:   Optional[Any]	  =	nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T							)							)
    a		:   Optional[Any]	  =	nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T							)							)
    a		:   Tuple	  =	nn.Parameter(
        torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']							)							)

    a		:   Any	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T							)							)
    a		:   Dict	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T							)							)
    a		:   List[Any]	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T							)							)
    a		:   Dict	  =	nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']							)							)

  a		:   Any	  =	nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']							)							)

  return model
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Dict	  =	nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T							)							)
  a		:   List[str]	  =	nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T							)							)

  a		:   Optional[Any]	  =	nn.Parameter(
      torch.FloatTensor(weights['Embed_0']['embedding']							)					,     requires_grad=_A							)

  a		:   Union[str, Any]	  =	nn.Parameter(
      torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T							)							)

  for lyr_num, lyr in enumerate(model.decoders							):
    a		:   Any	  =	weights[f"""layers_{lyr_num}"""]
    a		:   int	  =	nn.Parameter(
        torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']							)							)

    a		:   Optional[int]	  =	nn.Parameter(
        torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T							)							)

    a		:   str	  =	ly_weight['self_attention']
    a		:   List[str]	  =	nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T							)							)
    a		:   List[Any]	  =	nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T							)							)
    a		:   Tuple	  =	nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T							)							)
    a		:   List[str]	  =	nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T							)							)

    a		:   Any	  =	ly_weight['MultiHeadDotProductAttention_0']
    a		:   Optional[int]	  =	nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T							)							)
    a		:   Optional[int]	  =	nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T							)							)
    a		:   Any	  =	nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T							)							)
    a		:   Optional[int]	  =	nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T							)							)
    a		:   Dict	  =	nn.Parameter(
        torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']							)							)

    a		:   Tuple	  =	nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']							)							)
    a		:   List[Any]	  =	nn.Parameter(
        torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T							)							)
    a		:   Tuple	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T							)							)
    a		:   int	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T							)							)
    a		:   Dict	  =	nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T							)							)

  a		:   Optional[int]	  =	nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']							)							)

  a		:   str	  =	nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T							)							)

  return model
def  lowerCamelCase__			(		_A							):
  a		:   Optional[int]	  =	checkpoints.load_tax_checkpoint(args.checkpoint_path							)
  a		:   Tuple	  =	jnp.tree_util.tree_map(onp.array					,     _A							)

  a		:   int	  =	[
      'from __gin__ import dynamic_registration',
      'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
      'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
      'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
  ]

  a		:   str	  =	os.path.join(args.checkpoint_path					,     '..'					,     'config.gin'							)
  a		:   Tuple	  =	inference.parse_training_gin_file(_A					,     _A							)
  a		:   str	  =	inference.InferenceModel(args.checkpoint_path					,     _A							)

  a		:   int	  =	DDPMScheduler(beta_schedule='squaredcos_cap_v2'					,     variance_type='fixed_large'							)

  a		:   Any	  =	SpectrogramNotesEncoder(
      max_length=synth_model.sequence_length['inputs']					,     vocab_size=synth_model.model.module.config.vocab_size					,     d_model=synth_model.model.module.config.emb_dim					,     dropout_rate=synth_model.model.module.config.dropout_rate					,     num_layers=synth_model.model.module.config.num_encoder_layers					,     num_heads=synth_model.model.module.config.num_heads					,     d_kv=synth_model.model.module.config.head_dim					,     d_ff=synth_model.model.module.config.mlp_dim					,     feed_forward_proj='gated-gelu'					,     )

  a		:   str	  =	SpectrogramContEncoder(
      input_dims=synth_model.audio_codec.n_dims					,     targets_context_length=synth_model.sequence_length['targets_context']					,     d_model=synth_model.model.module.config.emb_dim					,     dropout_rate=synth_model.model.module.config.dropout_rate					,     num_layers=synth_model.model.module.config.num_encoder_layers					,     num_heads=synth_model.model.module.config.num_heads					,     d_kv=synth_model.model.module.config.head_dim					,     d_ff=synth_model.model.module.config.mlp_dim					,     feed_forward_proj='gated-gelu'					,     )

  a		:   Union[str, Any]	  =	TaFilmDecoder(
      input_dims=synth_model.audio_codec.n_dims					,     targets_length=synth_model.sequence_length['targets_context']					,     max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time					,     d_model=synth_model.model.module.config.emb_dim					,     num_layers=synth_model.model.module.config.num_decoder_layers					,     num_heads=synth_model.model.module.config.num_heads					,     d_kv=synth_model.model.module.config.head_dim					,     d_ff=synth_model.model.module.config.mlp_dim					,     dropout_rate=synth_model.model.module.config.dropout_rate					,     )

  a		:   str	  =	load_notes_encoder(ta_checkpoint['target']['token_encoder']					,     _A							)
  a		:   List[Any]	  =	load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']					,     _A							)
  a		:   List[Any]	  =	load_decoder(ta_checkpoint['target']['decoder']					,     _A							)

  a		:   Optional[int]	  =	OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'							)

  a		:   Optional[Any]	  =	SpectrogramDiffusionPipeline(
      notes_encoder=_A					,     continuous_encoder=_A					,     decoder=_A					,     scheduler=_A					,     melgan=_A					,     )
  if args.save:
    pipe.save_pretrained(args.output_path							)


if __name__ == "__main__":
     lowerCAmelCase:   Optional[int]					=		argparse.ArgumentParser()

     parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
     parser.add_argument(
         '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
     )
     parser.add_argument(
         '--checkpoint_path',
         default=F"{MODEL}/checkpoint_500000",
         type=str,
         required=False,
         help='Path to the original jax model checkpoint.',
     )
     lowerCAmelCase:   Dict					=		parser.parse_args()

     main(args)




'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    int		,     __snake_case					:    Callable		,     __snake_case					:    Optional[Features] = None		,     __snake_case					:    str = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    Optional[dict] = None		,     __snake_case					:    Optional[int] = None		,     **__snake_case					:    Optional[int]		,     ):
      super().__init__(
          features=__snake_case		,     cache_dir=__snake_case		,     keep_in_memory=__snake_case		,     streaming=__snake_case		,     num_proc=__snake_case		,     **__snake_case		,     )
      a		:   List[Any]	  =	Generator(
          cache_dir=__snake_case		,     features=__snake_case		,     generator=__snake_case		,     gen_kwargs=__snake_case		,     **__snake_case		,     )







    def      lowercase_			( self					:    Any      ):
      # Build iterable dataset
      if self.streaming:
        a		:   Optional[int]	  =	self.builder.as_streaming_dataset(split='train'      )
      # Build regular (map-style) dataset
      else:
        a		:   List[Any]	  =	None
        a		:   Any	  =	None
        a		:   Optional[int]	  =	None
        a		:   List[str]	  =	None

        self.builder.download_and_prepare(
            download_config=__snake_case		,     download_mode=__snake_case		,     verification_mode=__snake_case		,     base_path=__snake_case		,     num_proc=self.num_proc		,     )
        a		:   Dict	  =	self.builder.as_dataset(
            split='train'		,     verification_mode=__snake_case		,     in_memory=self.keep_in_memory      )
      return dataset